Good evening everyone and welcome to the Quarter 2 FY 2025 webcast of the Investor Day to discuss the business and the financial updates of LatentView Analytics Limited. LatentView Analytics is today represented by the CEO of the company, Rajan Sethuraman, whom we shall be referring to as Rajan during the course of this webcast, and we also have the CFO of the company, Rajan Venkatesan, who we will be referring to as Raj. We will start the session with a brief update on the business which will be given by Rajan and then followed by an update on the quarter two financials given by Raj. I would like to remind you that anything that is mentioned on the call that reflects any outlook for the future or which can be construed as a forward-looking statement must be viewed in conjunction with the risks and uncertainties that we face. These risks and uncertainties are included, but not limited to what we have mentioned in the prospectus filed with SEBI and subsequent annual reports that you can find on our website. Having said that, I'll now hand over the floor to Rajan. Over to you. Yeah. Thank you, Pranav, and welcome everybody to the webcast and the interaction today that you're lined up for our investors. Today being the first time that we are doing in- person and physical events, we also wanted to use the opportunity to provide a little bit of perspective about our journey so far and what we have in store for the future from a strategic perspective. So that's what we're going to be talking about. So we're going to give a little bit of the market outlook, we'll talk about our journey so far and then what our growth strategy is going forward. So firstly, looking at the market itself, you will all recall that we had shared a bit of that perspective as part of our IPO and as part of the DRHP that we had put out at that time. So we have done a little bit of refresh of that in terms of where we see the market today and how that will lead to the kind of headroom that we see for growth in the coming years. We still see that there is a fairly aggressive amount of opportunity that is there and the number that we had put out at that time, if you remember, was about $160 billion+ by 2026. We see that that is still playing out. The market size for data analytics itself has been growing quite rapidly and that growth is continuing and we believe that that growth is a secular trend that will play out in the coming years as well. We also see that there is a lot of new technologies that are coming to the fore. I mean, we have been talking about artificial intelligence and generative AI as well. So these things will continue to power what the opportunity is going forward. In some sense we can split the time frame that we have been in existence as well in line with how things have played out over the market. So if you look at traditional analytics that got done from say 2010 to 2018, it was all really about how do you solve a particular user's challenge or problem, maybe within a business unit or within a functional area as part of a particular value chain, and how do you look at data that they had access to, mostly structured data at that point in time, but data that was there within the enterprise. How do you look at understanding a little bit more about what has been happening within the organization and why it has happened? What we call typically diagnostic and descriptive kind of scenarios and trying to get that better understood, not just in the form of data and tables, but also visualizations that can help organizations consume and understand and appreciate that better. From 2018-2020 there was an inflection point and there were several technologies that started coming to the fore. Not that these technologies didn't exist in the past, but a coming together of different kind of developments, right? One, in terms of the cost of memory and compute becoming lower and newer technologies available to organize and process data meant that some of the approaches and algorithms, right, that mathematicians and scientists knew could now be brought into the world of business as well. Things like cloud computing, AI/ML, right, they started driving the action. You see the emergence of a lot more of these technologies playing a role in helping companies understand not just what happened and why it happened, but also in terms of predicting a bit more about the future, what is going to happen and therefore what can companies do about them. That's where a lot of the stuff started moving. Today from 2020 onwards you are seeing the emergence of AI-led analytics. Artificial intelligence is the buzzword today. Everybody is talking about AI and generative AI, and the interesting part of it is that it is no longer just in the realm of mathematicians and scientists or even just businesses. Today you are also seeing how consumers are very well versed with it, right? Everybody has used ChatGPT for something or the other, and we are all familiar with the capabilities that it brings to the table. Even school-going kids know how to play around with it at least. Right? And I'm sure that they've been using it for very interesting use cases themselves. So today you have a scenario where there is a bit of a culmination of all of these technologies and the trends that have played out. And organizations feel a lot more comfortable with accessing and analyzing data across the enterprise, structured data as well as unstructured data, data from other parts of their ecosystem, including their vendors, customers, and so on, and then being able to actually look at all of that information to solve the kind of business problems and questions that they are going after. In some sense, we believe that today the evolution is from a scenario where IT and IT systems, we are supporting what I would call systems of record to one where we are now moving to systems of decision making. Right? And these systems of decision making will heavily leverage the kind of technologies that they have talked about. So data and analytics will move from being an enablement and a support function to one which will become fairly central to the strategy of the organization to drive business growth going forward. Now, as an organization, we have been aware of what is happening right in the market and we had kicked off several initiatives as well as changes so that we are better prepared for addressing the opportunity that the market presents. One of the things that we looked at was like, which aspects of data analytics are emerging? So AI/ML and advanced analytics, the data visualization that I talked about earlier, data engineering, these are the different parts. And what we want to share here is that what is the composition between these different types of work and what is the kind of growth rate that we see happening in each of those areas? Of course, AI/ML and predictive, prescriptive advanced analytics will be a fairly significant chunk in the times to come. It's already more than 1/3. We believe that it will grow very fast in the coming years as well. I did mention earlier in an answer to another question, that data engineering is at an inflection point and how it will be very important for organizations to have very robust data platforms to power their analytics. So again, we see a lot of growth happening in that space. If you look at an industry perspective, we all know that segments like banking, financial services and insurance and retail and CPG have been the forerunners when it comes to using information technology. It is no different when it comes to data and analytics as well. They actually form a fairly big chunk of all of the money that is being spent and the work that is happening in data analytics. Segments like technology and healthcare, they come behind them. We see good growth happening in these sectors as well. I mean, both because BFSI as well as retail and CPG are expected to see fairly aggressive growth rates right in the coming year. So this is a bit of the market perspective in terms of how we see different types of work and industries stacking up. Now our response also has been to make sure that we align with that one other aspect. I think the one point that I wanted to add just on the earlier slide was, you know, while we are very, very strong on the technology side, right, and that as you can see, is probably about 12% of the total market spend with the Decision Point acquisition, plus also some of the solution building that you're doing specifically. You know, Anup spoke about product innovation, right? Smart innovation. So we are investing heavily into the retail CPG domain, which is one of the largest spenders for analytics initiatives. BFSI. Again, while some of you may have had a chance to look at our most recent earnings, while historically BFSI did not grow at a pace that we would have liked it to, but this year specifically we should end the year with about 10% contribution from the financial services vertical. So we are seeing a lot of traction coming in from both FS as well as CPG. And we hope that in the years to come, in the next three to five years, as we present our growth strategy, these verticals, in addition to TMT, which is expected to grow at a steady rate, will significantly contribute in our growth journey. No, thanks for adding that, Raj. So the other aspect has been how is outsourcing panning out as far as data analytics is concerned? And there was a question earlier, right. In terms of core processes and non core processes and how organizations look at them. There are different type of players in this market. We have pure play analytics companies like ourselves. And you would have seen some of the other names that we have indicated there and how they are panning out in terms of their market share and the growth that is happening in the space. There are large IT services companies, systems integrators that also play within this data analytics space and what kind of market share they have. Right. And how they are growing within the space. And then there are business process management companies. I mean these people have had access to a lot of data about the processes, in so many instances non-core processes, but also organizations that have been supporting core processes. Right. And how they are panning out. And then finally you also have large, you have consulting firms, the likes of a BCG for example, or a McKinsey. Right. And they have been part of this data analytics landscape as well. Right. Coming in more from the business strategy and how do you help connect the dots. Right. With data analytics. Each of these segments are experiencing different kinds of opportunities and growth rates. And then there is a certain kind of positioning that we enjoy as well. Our kind of take on the matter is that the pure play data analytics companies are the ones that are able to marry and connect the dots a lot better between the business problems and what organizations can do with their data, and in many instances helping them think through their analytics strategy in terms of the initiatives that they should be undertaking. So we believe that the growth as far as this segment is considered will continue to accelerate in the coming years. Now this is a bit of a market perspective that I wanted to give. Now looking back at our journey so far. Again, we wanted to talk about the different evolutionary stages or what I would call as orbit shifting moves that we have undertaken in the last 18 years of our existence. Early years from 2007 to 2014. Right. We really looked at how do we build partners with some of the largest brands that are out there. I mean by stroke of luck, I mean we started off with the technology ecosystem, working with some of the large players in technology and the digital native realm. And that kind of helped us gain a foothold in the U.S. market as well. And even today, a lot of the work that we do and the revenue that we get comes from the technology and the digital native ecosystem. One other orbit shifting move that we made at the time was to bring in a professional CEO. Right from 2010 onwards, my predecessor became the CEO, taking over from Venkat who was the founder CEO right till at that point in time. Those are really good years. I mean we grew very aggressively. We featured nine times in a row on the Deloitte Technology Fast 50 in India. And it was a period of fairly aggressive growth and expansion for us. 2015 to 2020 onwards, I see as a period where we did make again a few orbit shifting moves, but it was also a phase of consolidation for us and then putting in place the kind of platforms that we needed to capitalize on the opportunity that we saw ahead of us. So we launched and reorganized ourselves by industry verticals starting the year 2016. We also looked at how we can strengthen our operations and make sure that we are having more broad based kind of an approach to delivering client services. And we did see recognition from external partners during that period as well. Now this phase that we are currently in, from 2021 onwards, since we went public, our IPO was in November 2021. That's been a continuing journey of evolving from being an analytics execution partner to an analytics thought partner and a consulting partner in some sense. A lot of the things that we have been doing in these last three years have been about how do we go to market more on the back of specific solutions and value propositions and how do we get a seat at the table ahead of even companies thinking through what should be their analytics initiatives and partnering and helping them think through the book of initiatives that they should be executing. And of course, this period is also seen as being active on the inorganic front, with our first acquisition of Decision Point happening four months ago. Going through these evolutionary stages has also meant that we've been able to build the capability and the muscle required right for us to be a fairly aggressive player in this space and capitalize on the opportunities that are available now. The Decision Point acquisition itself has been a very interesting and important one. I talked about how we are pivoting more towards a value proposition and a solution led kind of an approach. Earlier we had identified that areas like R&D and innovation. We touched upon that earlier with the smart innovation talk supply chain. These were very important areas. We also saw revenue growth management as a very important topic of concern for consumer packaged goods companies. And that is what we have been able to address, and that's the gap that we've been able to plug through the acquisition of Decision Point. Decision Point also brought in capabilities on the generative AI front with the BeagleGPT as a solution, and we have been able to capitalize on that as well. This is also a bit of a foray for us into the product space. So while over the last 18 years LatentView has built multiple solutions and I would call assets and accelerators, we had never really truly graduated to a product. What BeagleGPT does is to bring that kind of a thinking as well into our spectrum of services. And we have been able to now look at the growth that we can drive in the product space as well. The third thing that we now have on the offer is a presence in Latin America, not just in terms of addressing the market opportunity there, but also Latin America being a base where we can support our clients out of from a nearshore perspective. So there's a lot of action and we already have a few of our clients starting to make use of LatAm capabilities and resources right for some of the work that we do for them. Decision Point also brought in a lot of marquee logos that they were already working with in the CPG space. So this has meant that we actually have dramatically upshifted in terms of the volume of work, but also the nature of the companies and the logos that we are working with. Right. In the CPG space. And then I talked about the access to the talent. Right. That's available within the Latin America market. So all these things are really helping power the action that we see in the CPG space. Now we are very keen to move forward with fairly strong top line and bottom line performance. Raj, do you want to touch upon it? Yeah, thanks, Rajan. Coming to the financials, which I'm guessing would be of the most interest for the audience today. What we tried to present over here is of course, while we don't have a full year data for FY 2025, you know, we have the last 12 months data. We wanted to present data from a year before the IPO and where we are today. Right. It's impressive what we've been able to accomplish over this four and a half year period in terms of just top line growth, the numbers that you see for the year before the IPO. FY 2021 was the full year before the IPO; we were at about 41.4. So roughly we're at about 2x that size. But if you just take the IPO year as a benchmark, we are at about 1.6x-1.7x that size. Right. As we stand today. Most of this growth, except for the last quarter where we consolidated the Decision Point numbers along with us, this entire growth has been on the back of organic initiatives that we've driven. In terms of the EBITDA profile for the business, I want to spend a little time over here because I think while the pre-pandemic days we were operating at levels of EBITDAs in excess of 30%. Right. Those were also periods that you'd have to understand from the way the delivery model was moving. Clients were very, very comfortable because people were working remotely. There was hardly anyone coming to office. So there was a significant shift of the delivery teams to an offshore model. I remember we were almost skewed to a one is to six sort of a ratio at that point in time. Where for every one person who is co-located at a client location in the United States, we had six people working out of India. So which automatically meant that your gross margins were significantly better at that point in time. So this was a phenomenon that played out in COVID and the year after COVID. So that's one aspect. Secondly, at that point in time I would say it was also, as would be the case with most companies that were battling with demand concerns around COVID, we also, I would say, in some sense, tightened our belt, right? We wanted to conserve cash flows. We wanted to optimize costs, which necessarily meant that on the business development side, as well as corporate and enablement functions, there was a lot of tightening that happened around that time in terms of spends, which automatically meant that most of the incremental revenue that was coming in was all flowing through to the bottom line, because there was really very minimal S&M spends as well as G&A spends at that point in time. So in some sense, the FY 2021 and FY 2022 EBITDA margins were not, I would say, sustainable margins in some sense, because the level of offshoring that was happening as well as the SG&A are not representative of where the business is today. Now, clearly on the back of the IPO, one of the promises that we did make was we will expand our footprint in Europe, which meant that we added significant business development presence in the region. What we also did was U.S., which is our primary market. We added a lot of, I would say, firepower in the front end growth engine, which was the hunting team. Just as a point of reference, we had close to about three sales folks at the time of the IPO. That number from three went up to about 16 people in the United States and close to about nine folks in Europe. A lot of those investments that we did played out in the year FY 2023, which is when you started seeing the margins drop because those investments were happening. But the full impact of those margins or those investments really played out in FY 2024. You will remember that the beginning of the last year, our EBITDA margins dropped to as close as 19.1%. But it was also something that we had clearly guided the market on. We were saying that we were investing for growth and not just on the sales and marketing side. It was also on solution building for the future. Because clearly these investments, according to us, were necessary for us to differentiate ourselves in the market. But what you will see in the most recent period is we have started seeing an upward trajectory in that margin. In fact, for the most recent quarter, our adjusted EBITDA margins were close to about 22.5%. We expect this trajectory to go up as we get into H2. What we also very strongly believe is the level of investment that is there in the business today is good enough for it to sustain for the next, I would say even 25 -30, 25 to maybe $50 million of growth, so we don't need to make significantly higher level of investments. So a lot of the growth that will come now should ideally generate operating leverage. And you will see that playing out in the margins. Now, it's also one thing to say that, yes, the margin should expand, but we will continue to invest wherever we see data engineering is one big focus area. For instance, our partnership with Databricks is something that we announced. We will continue to invest in these pockets because again, we believe this will be significant growth drivers for the company as a whole. And a lot of that EBITDA margin is again, reflected in, in the way our PAT margins have also played out. The one thing, though, that I would like to call out specifically with regards to the PAT is the two big delivery centers. In fact, the delivery centers that we have in India is one in Bangalore and the other one is in Chennai. Chennai, of course, being the largest delivery center. Our SEZ benefits for the delivery center expired last year. Right. Which is why you will see that PAT as a percentage of revenues came down because we lost the tax benefits associated with that SEZ. But all in all, I would say the margin profile of the business is continuing to look good. We're very happy that we were able to deliver our seventh consecutive quarter of sequential revenue growth. And with the Decision Point acquisition today, we are hoping that we will end the year with close to about, on a pro forma basis, between $100 million and $105 million in revenue. So if you take that into account and compare it with where we were in the IPO time frame, we've grown two times from that time. And so our goal also is to get to a certain number which Rajan will talk about when we talk about the strategy part of it. I'm going to hand it back to Rajan now to talk about the strategy. Cool. Thank you. So, yeah, moving forward, how do we look at things panning out for us in the coming years? First off, right, we want to get to a $200 million-$220 million target by financial year 2027 or financial year 2028. Right. Thereabouts, you will see that this will be a doubling of where we are likely to end this year at. Right. In some sense, bit more than the doubling of where we are likely to end this year at. So we would have grown about 2.5x from the time that we did the IPO. We are expecting to double again, right. In that kind of a time frame. And we believe that that opportunity is there in the market. Right. In terms of what we see as headroom for growth. Earlier I had presented what are the horizontals, the type of work that are panning out as far as the market is concerned and where the expected growth is. What you see here on the slide is like what is our composition of the work. You'll see that AI/ML advanced analytics work is close to 1/3. For us, data engineering is about 20%, 1/5 of the work that we do today. About half of the work that we do today is in the area of data visualization and generating insights and doing data analytics. The growth is obviously expected to be really high on the AI/ML and the advanced analytics front and data engineering as well. In fact, I believe that for us, data engineering can be close to 35% or even 40% of our revenues with the right kind of focus in the next two, three year time frame. From an industry standpoint, again, our presence in the high growth sectors of BFSI and retail is quite low. We are very heavily indexed on the technology and the digital native space, the TMT space. And as Raj mentioned earlier, that is the one where we have had our biggest growth vectors. However, we believe that a lot of the work that we do on the technology and the tooling and the understanding that we bring because of working with the best companies in the technology in the digital native space, we'll be able to bring it to bear on the CPG, on the BFSI and the retail space. In fact, the acquisition of Decision Point and shoring up on the quantum and the nature of the work that we do in the CPG is also a further commitment to really driving the action right as far as the CPG space is concerned. So we are expecting that growth in those verticals, BFSI and CPG and retail, will be a lot more in comparison to what we will see as growth rate in the other sectors. And that will actually pave the way for the kind of doubling that we intend to achieve in a two, three year kind of time frame. The priorities that we will be focusing on in terms of making that happen, one is of course the work that we have been doing at the front end of the value chain, customer and marketing analytics. We believe that there is a lot more action that can happen on that front. So one of the things that we did when we started out this year was to actually build out, start building out a marketing analytics center of excellence where we bring that we can drive the next set of innovative ideas that organizations can look at. So from a holistic perspective on improving marketing return on investment. So how do we have conversations with the Chief Marketing Officer and their team in order to drive that action? So in this area particularly, I mean we have also made an investment, a significant investment in terms of asking our industry lead for the technology vertical to take up this new role so that we can really accelerate what we are doing in the marketing analytics space. So that's an area of focus for us. We will obviously look at generative AI. There is a lot of conversations and interests today. When I look at the opportunity pipeline that we have, over 20%-25% of them are related to generative AI opportunities. There is still a lot of experimentation that is still going on, but we are also starting to see that some of the more mature organizations from an analytics perspective are starting to move from just doing experiments with generative AI to actually putting production systems right in place, and that automatically means that the spend and the size and the impact of those initiatives will be much larger, and that's what we are starting to see, so we have already started winning deals which are $1 million or more just on the generative AI itself. There is a lot of experimentation going on and I'm sure that that will continue. But in the coming quarters we are expecting that there'll be a lot more initiatives that start to see production. I talked about data engineering earlier, how it will be a very important area of focus for most organizations that are moving from addressing the low-hanging fruit to the more complex initiatives. This is an area that we intend to double down on. We had partnerships with some of the players in the space earlier, right, including Snowflake, Fivetran for example. Today there is a lot of action happening with Databricks and just a month ago we were elevated to the highest partnership tier as far as Databricks is concerned. We are already having multiple conversations with them with their leadership, industry leadership, as well as their alliances leadership. And we believe that the Databricks data engineering opportunity will be a big space for us to capitalize on. We are also investing in this space in terms of shoring up our own alliances team as well as the number of people that we will have who are trained, certified and building the kind of solutions that can be hosted on the Databricks platform. Also we also looking at nearshore centers. I talked about how Latin America, with the new capability that we now have, can actually serve as a talent base for our clients in North America. We are starting to see quite a bit of traction there and we will be doubling down on that going forward. We've had questions earlier also about what we are doing in Asia Pacific, in India. We have done a few experiments over the last 12 months or so, and at this point in time, we have decided that we will double down specifically on the opportunity. In the Global Capability Center space, there are many organizations that either have a subscale analytics capability or they are looking to set up a new capability. They might have an IT services capability, but not analytics capability. And then there are of course organizations that are looking to augment the capability that they already have. So we do have different kinds of models that we are taking to them in terms of how can we be a very credible partner, whether they are looking to set up a capability, or whether they want to transition their subscale capability into a more effective kind of a model, or whether they're just looking for a partner to augment what they are doing. We are starting to see some good traction on that front. And this is an area that we will really double down on in the coming quarters. And finally, I think all of this work happens only because of the high caliber talent that we have within the organization. So this is an area that will continue to be a focus in terms of how do you build out the kind of skill sets and stay at the cutting edge in terms of the new technologies and the developments that are happening in this very dynamic space. And this is an area that we doubled down on to make sure that the supply side is well addressed as well. I mean, all the other five points that I talked about were really related to good market and creating the demand for the kind of services that we provide. But we want to make sure that right on the supply side as well, we are well positioned. So broadly from a management and a leadership standpoint, these are the strategic priorities that we have over the next three years. We believe that if we execute well on them, there is enough headroom for growth and there is enough opportunity. We believe that we can achieve the doubling of the revenue that I talked about right in the two, three year time frame if we were to execute well on them. I think that kind of brings us to the end of our initial introductory remarks, right on our performance and how we see things going forward. We will be happy to take questions right now. Thank you so much. Rajan and Raj. Can I please request Rutuja, who is the moderator, to please unmute the first participant and let them ask the question? Thank you very much. We have a first question. We will be taking the audio question, which is from the line of Vinod Vaya from Anand Rathi PMS. Please go ahead. Vinod Vaya may be requested to please go ahead with your question. The line is unmuted. As there is no response from the line, we will take the next question, which is the text question, which is from Suraj Malu from Catamaran, and there are three questions in the line. First question, which says, can you please help understand what is the work that LatentView does for key clients like Adobe, Microsoft, Google? Second, is LatentView involved in development process of ChatGPT or Bard? Third, in the presentation you mentioned NVIDIA as a technology partner, can you please help understand the partnership with NVIDIA and the kind of work done along with it? Okay, I'll take this question. So firstly, on the nature of work that we do for some of the large technology clients, it's a fairly broad spectrum. I mean a lot of the work that we do is at the front end of the value chain, customer analytics and marketing analytics, right? Whether it is about segmentation, micro segmentation or identifying cross sell upsell opportunities, questions around loyalty and personalization, customer lifetime value, helping organizations think through their media mix strategy, looking at marketing return on investment with respect to the spend that they are doing on promotions and campaigns. This is the mainstay of the work, right? And the technology clients that you mentioned are no exception. We actually support them on many of these topics. We also help with quite a bit of problems related to fraud and risk analytics, right? For example, how do you identify whether a transaction that is in progress on a platform, it could be an e-commerce platform or it could be a ride-sharing platform, whether that is a genuine or a fraudulent transaction? We help clients with that. We do quite a bit of work on the supply chain front as well. For example, with one of the leading manufacturers of printers and laptops, we have been helping use sensor data that come from all the instrumentation that they have done on these devices to figure out when that printer or laptop is likely to fail and what can you do about it from a predictive maintenance standpoint. So we do quite a bit of work related to demand forecasting, inventory management, spares optimization and the like. More recently we have also started working with some of these organizations on the people analytics front which is to help them think through how can they use data that they have about their employees and people to create better employee experiences. We talk about customer experience all the time. But just like how you can look at customer segmentation and customer lifetime value, you can also move to what I call an employee segment of one. And how do you create really powerful experiences for your employees in terms of all the different HR aspects that you touch upon. So that's the broad spectrum of work that we do. I'll answer the third question. I can't recall the second one. The third question was about the partnership with NVIDIA. NVIDIA, you all know, is a fairly significant player today. In fact, they have risen to prominence fairly rapidly and especially so in the last year, 18 months, just because of the quantum of data that organizations have today and the fact that they need ever more higher orders of computing power to process all the data that's available. In some sense, the use cases that are interesting and relevant to the NVIDIA partnership are the ones where you are dealing with very large volumes of data, where you need real time analysis and analytics to support decision making, and which requires a very high degree, a high volume of computing as well. So those are the kind of use cases that appeal to that partnership, and those are the use cases that we are evaluating. So, for example, let's say that there is a football match happening in a stadium and people are sitting in different parts of the stadium and you know that there are these billboards that are there around the stadium. What kind of advertisements do you show when a particular play is in progress in the football match? And who do you want to show that? That could be an interesting use case. You can take the same use case and extend it to an airport kind of concept. I mean, we all go through airports all the time and then we see these different billboards, right? What advertisements do you see, for example, when you're boarding, when you're waiting to board a particular flight? Could depend on who is standing there in the queue, right, from which city to which city it is, and therefore, what kind of ads do you want to show? So there are very interesting use cases which require you to analyze data that is available then and there and then provide good quality of insights. So those are the kind of use cases that appeal, right, for that NVIDIA partnership. And we are working with them to identify and implement those use cases, right. Either as experiments or proof of concept, so that organizations that have those use cases, right, will be able to capitalize on them. Can you remind me of the second question? Do you work with? Yes, the second question. Oh, yeah, yeah, sorry, yeah. The question was whether we have been helping them build out Bard and ChatGPT. No, the answer is no, we are not involved in building out the LLMs and ChatGPT. What we help organizations though is how do you make use of those technologies, so today, if there is a Gemini or a Llama or other kind of LLMs available in the market, how do you make use of them to address your business problems? That's what we focus on, and that itself is a big gap. I mean, there is always a big gap between what is available in the form of latest technology right in the market and how can you make it relevant to your business problems, so we focus on understanding some of these technologies and then bringing into life in the business context. The good news though is that because we work extensively with the technology ecosystem, we get exposure to these technologies well ahead of them becoming available even in the public market. So we have worked with the precursors of a Gemini, right? We have worked with some, some other transformer models that Google was experimenting with. And since we have that upfront understanding, we are able to bring the power of those technologies to many clients right, as soon as they become available for public consumption. So that's what we focus on. Yeah. Thank you. Rutuja, can we have the next question please? Yes, the next question is from Mithun Aswath from Kivah Advisors. And the question is, is the doubling of revenues based on organic? Now, I wouldn't say that it'll only be organic growth. I did mention that data engineering is going to be an important area of focus. So right now our M&A efforts are focused on two things. One is the successful integration of the first acquisition that we have done, Decision Point, and making sure that we are realizing all the synergy benefits. The other thing that our corporate development M&A team is focusing on is to identify targets within the data engineering space with heavy emphasis on Databricks because that's the partnership that we are also investing in. And I believe that some of the growth that we are looking for will come from the inorganic route as well. Now, I don't know yet in terms of how much of that will play out. We believe that the organic growth opportunity itself is fairly significant, but we would be keen to also supplement that with the inorganic route. Rutuja, can we have the next question please? Yes, the next question is from Pratap Maliwal from Mount Intra Finance. And there are the two questions. First, can you provide some more color on data engineering vertical and the type of work we do in the segment? And the second is what will be the likely growth rates of our main areas of the descriptive prescriptive and data visualization analytics given the effect of GenAI on this areas? Yeah, sure. So on the type of data engineering work we do, I would have to provide a little bit of context in the sense that our data engineering practice is born out of the need to make sure that the use cases that we are working on are delivered in a very effective manner. So when we started moving up the maturity curve in terms of the complexity of the analytics use cases that we were starting to address, that's when it became apparent that many organizations don't have a great data ecosystem. I mean, they have data which is very fragmented and which is residing in multiple silos. And while that is okay when you are trying to solve for a local optimization problem, working with data within a particular part of the enterprise, the moment you move on to more complex initiatives that no longer suffices. So much of the data engineering work that we do is really about how do you create those data ecosystems that can help you do the more complex analytics that you're looking to do. So quite a bit of the work is around moving data into those kind of platforms, designing, architecting and building those platforms. Of course, we use some of the latest tooling and technology that is available, and providers as well, like a Snowflake or an Azure or a GCP or AWS, for example, and Databricks of course, which I've been talking about. So our people bring that kind of design and architecting capabilities to the table so that we can help build those platforms in which you then bring in the data and then you do the analytics on top of it. I'll give you an example, right. That might help illustrate. We've been working with one of the largest food logistics company right in the U.S., and they are a company that has been created through the acquisition of some 35 different operating companies over the last, I don't know, two decades. The challenge when you grow through acquisition like that is that each of those companies that you acquired, they come with their own data ecosystem, their own legacy, and they have organized and worked with data in different ways. Now today, when you want to offer a more holistic kind of a customer experience, leveraging all of the things that these different operating companies do, you can do that only if you have a grasp of all the data as well within these different operating companies. That's what we have been helping them with, right? Creating a data platform that can pull data from all of these different ecosystems and operating companies so that you can provide very powerful recommendations. When the food truck goes to deliver, whether it is green grocery or whether it is other kind of produce, no meat and poultry and so on, the truck driver is in a position to make recommendations to the shop that are delivering to. So let's say that it's a pizza outlet that this truck is going to and you know that that pizza outlet today is buying only flour from you, right? They don't buy cheese, they don't buy olives, they don't buy other things. Right. How can you make recommendations at the right price point and the right kind of inventory replenishment kind of mechanism so that the pizza outlet will start buying more from you? Right. So that's the kind of capabilities that can be created. Right. If you have the right kind of data platform, especially in the context of ecosystems. Right. That have very disparate type of data silo. So that's the nature of the work that we do on the data engineering front. So remind me of the second question, second part of the question. Rutuja, can I request you to state the second question again please? Sure. The second question was what would be the likely growth rate of our main areas of descriptive or prescriptive and the data visualization analytics, the effect of GenAI on these areas. Right. So we had actually shared a bit of that perspective earlier. We are expecting that there will be a lot more action on the predictive prescriptive analytics and the data engineering front. GenAI is probably going to solve a lot of problem statements and make life lot easier on the diagnostic descriptive and the visualization part. Because diagnostic descriptive is always in the context of a historical perspective. Right? And you are really looking at structured plus unstructured data to answer those questions around what happened and why it happened. Similarly, I would believe that visualization capabilities in terms of figuring out what is the best way of representing a data generative AI capabilities will be really good. We already have implemented that using BeagleGPT. In fact, later on, people here, at least in the audience in this room, you will have an opportunity to understand what I mean when you go and take a look at the demo. Because for every kind of question there is always an ideal visualization. Right? That will make sense, and today the GenAI capabilities are already pretty good in terms of figuring out what is that ideal visualization that will make you understand and interpret that data. Right? So, therefore, my hypothesis would be that visualization, diagnostic descriptive analytics will probably start getting more automated through the GenAI kind of capabilities. Whereas data engineering as well as at least a more complex data engineering part, as well as the predictive prescriptive analytics will definitely require a human in the loop. The good part is that even in those areas, some of the routine stuff will get taken care of by the GenAI so that the human in the loop can focus on the more complex aspects of the business problem that they are trying to solve. So the growth rates will be higher in predictive, prescriptive analytics and around data engineering, whereas diagnostic, descriptive and visualization, it will start tapering off because a lot of the work will now get automated. Can we have the next question please? Thank you. The next question is from Hardik Ashar from Nuvama Asset Management. And the question is, while you have guided for doubling of revenue in next three years, what would be expected margin profile look like in terms of EBITDA and PAT? Obviously I did touch upon the topic of margins, right? For the most recent quarter you would see that on an adjusted basis. The adjustment really over here is for some of the transaction related expenses in relation to the acquisition of Decision Point. If you actually adjust them out and look at the core performance of the business as is, we are looking at a 22.5% for the most recent quarter, right. We expect that this should continue to improve. While for the first quarter we were in the 21.5% levels, we've improved that by 100 basis points on our adjusted basis for Q2. We expect this trajectory to be significantly higher in H2, where at least for H2 we should be in the range of 24%-25%. For the company as a whole, we expect that this sort of margin profile should continue for the next two years. So you can expect that the doubling of revenue should come in with margins between 24%-25%. That's the level that we would like to maintain. And anything that's over and above that. And if we see operating leverage playing out, as Rajan mentioned, we will continue to invest and invest for growth. Our primary objective is obviously to double that revenue and get to those numbers. Right. The margins. We believe we have enough levers as well as enough, you know, plays in hand for us to manage margins. I think at this point in time the focus is to get the growth to the 30% odd levels which we were recording around the pandemic times. So that's our priority number one. But margins, we expect it to settle down between the 24%-25% and anything over and above that will be reinvested back. Thanks. Those are all the questions from online. I'll now request people in the audience if they have any questions they can ask them now. Yeah, I wanted to understand our partnership with Databricks slightly better as to how does it enhance our existing set of capabilities you did mention about the ability to pull the real-time data. So can you elaborate more on such capabilities that come with Databricks? Yeah. So Databricks is doing a lot of the innovation there, right? In terms of the product that they have on offer, right? How it allows you to do so much of the analytics and the organizing and the governance, right? And all of that stuff that I talked about. So when organizations are now evaluating what is the right platform of choice for them to be able to drive their decision making and optimization better, Databricks is emerging as a pretty good option, right? I mean, not that everybody is on Databricks. I mean, there are many other legacy systems in place. I mean, and then of course, Microsoft Azure has been building out Microsoft Fabric, for example, right? As an alternative where they believe that they can offer the same kind of capabilities. Same thing is going on with AWS and GCP, right? In terms of the kind of ecosystem that they are building at this point, Databricks is like out in front in terms of the innovations that they are bringing under the right kind of cost points as well. Right. And efficiency points. So we believe that they have a good runway of growth over the next three to five years. And that is what we want to capitalize on by building our own expertise and capabilities in using Databricks as a product and helping organizations implement and realize the value from that implementation. Many of these products, there is a certain kind of licensing model that they have and it makes sense for an organization that has invested in that product to get the full mileage out of that investment. Right. And that's where we come in and help. I mean, you might have decided to buy Databricks and implement it in your enterprise. But then how do you realize the full impact and benefit of that implementation will depend on how well you are able to identify business use cases that you can deliver on, which either impact your top line or your bottom line. Right. And that's where we come in with our understanding of the kind of problems and opportunities that organizations can pursue. So we are shoring up on the bench strength of our Databricks and data engineering practice, the number of people who are trained on that, who are certified on that. We are also building out solutions and value propositions that can be used as assets and accelerators to make things happen. So if there is a particular kind of business problem that you're trying to solve, do we have an accelerator which can work within the Databricks environment that addresses that? It could be a supply chain use case, it could be a customer analytics use case and so on. So that's the kind of investment that we are doing. The expectation is that we will be able to ride on that wave, the growth wave that Databricks is experiencing in the market, by being a very credible partner to them in taking that solution. Now, Databricks has numerous other partners, right. So it's not as we are the only partner, but we want to be a very, very strong, credible partner. Right. That is not just doing execution, but is also bringing thought leadership. Right. In terms of driving use cases, Databricks will they measure themselves and their people based on the consumption of Databricks? Because the way it is priced and licensed is based on how much of the Databricks capability is being utilized by the client. And as I mentioned earlier, right. The flip side of whether the client is getting enough mileage from that is that if they don't, they're going to start turning off the tap. They won't pay that much for the license, they'll cut down on the number of licenses, they'll cut down on the number of modules and so on. And that's what Databricks wants to avoid. So the more use cases that can be identified and gotten onto that ecosystem, that's what they would be interested in and that's where we partner. Can you have the next question please? Hi. Thank you. You just mentioned an accelerator also that will help you deliver faster, I'm guessing. So would it be fair to say that you are working on improving the average revenue per employee? Okay, let me put it a different way. Is your revenue completely dependent on the number of employees that you're delivering that are being utilized or are you having ways that is increasing the amount of revenue per employee you're able to generate? If that is what you said, if that is true, then the revenue, the EBITDA margin increase that you're expecting over the next couple of years, how much of that will be coming because of the accelerators and how much of that will be coming because of general overheads o perating leverage kicking in? Yeah, I mean we haven't done the exact math to answer your question precisely, right. But you're right that, I mean, you're correct in the point that you're making. Right. The expectation from the investments that are going into our solutions, our value propositions and the accelerator is that we can create nonlinearity in terms of effort versus revenue and margins that we are getting. We have already seen good success with several of our solutions. I mean, earlier in the day we presented smart innovation and the work that we have been doing with that CPG company on the ice creams front, right. We have done similar work for other categories like beauty and cosmetics. Another very important solution where we have seen good amount of nonlinearity is on the data engineering front, right? Our data migration accelerator, MigrateMate, that's what it's called. That's been creating a lot of non-linearity. I mean there are instances where we have saved like up to even 40%-50% of effort on the back of using that accelerator. The endeavor would be to constantly keep finding those kind of examples where we can build an accelerator that will create that non-linearity. Sometimes the non-linearity is in terms of business development and convincing a solution, convincing a client to adopt a certain kind of an approach. Sometimes the non-linearity is in terms of the actual effort involved in executing on that project itself. That will be an important aspect of margin and revenue improvement right in the days to come. Operating leverage is also important. We want to make sure we are constantly looking at how well we are running the organization. So that will be an area of focus as well. We will, I think, maybe be in a better position to answer your question as we see some of these things play out there. Thank you. At this point of time, I'd like to thank all the participants from the webcast who joined us. I'd now like to take the next question, please. Yeah, thanks. Raj, in your guidance for this year, that is 2025, about $100 million-$205 million in revenues. How much of demand environment improvement are we building? Are we building any improvement in the overall demand environment? In the previous interactions you've highlighted that there is some, you know, hesitancy of, you know, new clients actually going ahead and spending, or rather. So how much of that are you building in that continuity or are you building in improvement in demand in the immediate near term? I have one more question after that. So I'll let Rajan answer the demand side of it. But just to sort of. You spoke about the $100 million-$105 million was the number that I gave, right? So that of course the $105 million is on a pro forma basis. Assuming we consolidated Decision Point for the 12-month period. The fact is that we consummated the transaction only towards the end of Q1. So we are consolidating their results for nine months. So the $100 million-$105 million is assuming we had 12 months of performance of Decision Point. So just wanted to clarify that. So for the $105 million, there is no need for a major improvement in the demand scenario. I mean, this is like something that we are reasonably confident of based on the current trajectory of what we see now. The second half of the year could turn out to be better though. Okay? I mean, in fact, we very recently won the largest ever single statement of work deal that we have in our history, amounting to more than $3.5 million on a per year basis. So this is like the biggest deal that we have done. There is another one in the works which is probably going to be close to $1 million or $1.5 million with another CPG company. And there are a few of these that are now starting to see the light of the day. I mean, I've been mentioning this in the past several quarters on how clients are still sitting on the sidelines and waiting to make those big commitments. Our expectation is that things will get better. So if that happens, there'll be a bit more upside. Right. On the numbers that we talked about. That's great to hear. My second question is on one of the slides that you presented on the competition cohort. Now I wanted to understand more as to how do we make sure we differentiate between our pure play IT services peers, wherein they have access to. Especially the IMS guys have access to the backend of the client. They possibly be in a better position because they have access to all the raw data, whatever that goes into the cloud because they would be doing cloud migration and stuff. How do we sort of make sure we differentiate against them? And the second cohort will be the consulting guys. I mean, they've been doing this since a long time. So how do we make sure we sort of go a step ahead against the Deloittes of the world? Thanks. Yeah, I mean, interestingly, I mean, the answer to that is going to be lying between the two, right? If you look, I mean, it's like the difference between accounting and consulting, right? I mean, IT services organizations, they do a lot of work in terms of building those systems of record. I mean, not to say that they are not supporting and helping organizations on process improvement and decision making and optimization, but their legacy, right? And their DNA really is around building, putting in those large systems in place, right? And that's the perspective with which they come. Consulting companies, on the other hand, they focus more on understanding the business environment, right? And figuring out benchmarking, for example, right. Across different enterprises in the same industry or even in other industries that are relevant to you, and then therefore figuring out what are the three, what are the next big trends that you need to be bothered about as far as your context is concerned. Now, the ability to marry the two and then bring the right kind of approach to how you can use the data and the analytics in order to drive those business strategies is where we play right now. That doesn't mean that these other organizations cannot do it. Right. I'm sure that people on the consulting side, if they, I mean they, many of them have already started creating those practices right now, whether it is a BCG GAMMA, for example. Right. McKinsey had a BTO, a business technology office. Right. Long time back. Deloitte does that capability. Right. They can all do that. It's also a question of price points, right. In terms of where do we play and what is the speed, agility as well as the depth of understanding that we can bring on the technical side to those conversations. So I think we have a sweet spot today in our ability to make all of that happen and that is what we are trying to exploit in the coming years. Conversations. We are seeing shifts though, in the sense that all of the work that we were doing, say up till like 10 years back or even like six, seven years back, much of it would have been conversations that we had with only business sponsors and business stakeholders. Today, some of the work that we do is really in terms of having conversations with the CIO, CDO organization, the Chief Data Officer, the Chief Analytics Officer. So some of that shift is also happening. But this has always been a bit of a holy grail issue for most companies, meaning that you build a lot of IT and systems. But when it comes to decision making, many of them still pull data into their spreadsheets and then they act on them. So I think that is the opportunity that is still playing out. Right. How do you put in better quality decision making and optimization systems? And today data analytics as a space sits at the top of that pyramid trying to bring together all of the stuff that is there in the ecosystem. And I think that's the play that's available for us. We'll be taking two more questions. Anyone has a question? Yes, ma'am. Please. Hi, good evening. I'm Grishma Shah from Envision Capital. Curious to know how would be our length of engagement with our customers increase, given that if we are accelerating the growth pace, one is to expand the capabilities, but the other is to expand the tenure that we work with the clients. So if you could highlight. Sure, yeah. So even today, 70% of the work that we do is what we call managed services. In a managed services engagement, we will have a master services agreement in place that can span anywhere from three, five. Right. Some of them are even indefinite, meaning that there's no end date. Right. And the MSA really determines the scope of what all we can engage with. Right. In the client organization, individual statements of work still tend to be a year long typically. And that is something that I'm expecting to change already. Some of the engagement that we are signing, they span larger time horizons, longer time horizons. Right. Two years or three years and so on. I'm expecting that as organizations move up the analytics maturity curve and they go after the more transformative, complex kind of global optimization rate as opposed to optimizing in a particular area, the length of those engagements will also become more. Right. And therefore I would expect that the tenure of each statement of work will increase. But having said that, even the construct that we have today, it is while we have to go through that effort and that activity of annual renewal, in most instances we have seen that it has been fairly smooth sailing. I mean, even if I take the time during my presence in LatentView right into account, which is the last eight and a half years, we haven't had too many instances where a client decides to cut down the scope of the work dramatically. Right. Any kind of cut downs typically tend to happen when we are only engaging with them in a fixed fee, fixed scope project. Right. And some early demises are there, meaning that we do one project and then we are out. But otherwise managed services construct, it's a fairly predictable kind of a model. Just to add to what Rajan said. Right. I think this time we've also, in addition to the usual metrics that we put out, if you look at our investor deck, there are a few others that we speak about. One of them is also the length of the relationships that we've had with clients of ours. And today if you just look at revenue that we generate from clients where we've had a relationship in excess of five years, so that's about 76% of our revenues today. So that should also give you a sense of the high degree of renewals that typically happen. And in general, I think we obviously can do a much better job of generating new logo revenue. But traditionally our strength has been once we land into an account, we're able to land and expand. I think farming is obviously our strength and that is also demonstrated in the revenue that we generate with clients where we've had contractual relationships over five years. We'll have the last question for today in case there are more questions. Perfect. Thank you. Thank you so much for all the questions that you asked today. We do have a large number of t he senior leadership of LatentView team here who's available in case you want to talk to them. Thank you so much, Rajan and Raj. Thank you all for your questions and being a patient audience. I'd like to also quickly tell you that we have the BeagleGPT demo there in case you want to personally try out what BeagleGPT product is like.